Quality and Analytics Specialist

Quality and Analytics SpecialistAbout Us We do things differently. We build a solution for enterprises to make sense of all of their information. We know how important it is for companies to understand their customers, so we provide our technology to solve their biggest challenges. We believe in open and transparent communication, not strict rules and hierarchies. We are a team of hardworking, talented people who aim to build software that makes sense of data. We’ve got some huge challenges ahead of us, and we need smart, driven wordsmiths to help us tackle them. If you think you’ve got what it takes—join us. Role Summary We are looking for a Manual QA Engineer specializing in Data Quality and Analytics Testing. This role requires strong hands-on experience in manual testing, data validation, and backend verification, along with practical exposure to Jupyter notebooks, Python, and data libraries for validation purposes. This is a QA role with heavy emphasis on manual testing and data validation, not a data engineering or data science position.. Key Responsibilities Manual QA & Functional Testing Perform end-to-end manual testing of UI/data and analytics features. Validate application workflows, UI behavior, and backend data processing. Design and execute detailed test cases, test scenarios, and regression suites. Perform exploratory, negative, and boundary testing. Pipeline & Platform Testing Perform manual testing of Spark/PySpark data pipelines. Validate ingestion from files, APIs, and cloud storage. Execute sanity, regression, and volume testing for large datasets. Notebook & Python-Based Validation Test and validate JupyterLab / notebook-based workflows. Execute and review notebooks for correctness, stability, and expected outputs. Use Python and data libraries (Pandas, NumPy, PySpark) to support validation and comparisons (not for model development). Validate analytical results and derived datasets. Data Quality & Validation Conduct source-to-target data validation using code and No code process. Validate transformations, joins, aggregations, business rules, and calculations. Identify data quality issues and inconsistencies with pipeline outputs. Test Documentation & Defect Management Create and maintain test plans, test cases, and validation checklists. Log, track, and verify defects using Jira or similar tools. Collaborate with engineering teams to reproduce and resolve data issues. Required Skills Must Have 3+ years of experience in Manual QA / Data QA / Backend Testing Strong understanding of QA processes, SDLC, STLC, and defect life cycle Excellent SQL for data validation and reconciliation Practical exposure to Jupyter notebooks / JupyterLab Experience using data libraries such as Pandas, NumPy, PySpark Experience with Jira or similar bug tracking tools Good to Have Basic test automation exposure Experience with cloud platforms (AWS / GCP) Exposure to Airflow or other workflow schedulers Exposure to geospatial datasets/tools (Sedona / GeoMesa) Note: This role requires strong experience in Manual QA and Data Quality testing. Candidates with only Data Engineering or Data Science backgrounds and no QA experience are not suitable.